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[GetPortalCommentsPageByObjectIdResponse(id=2110802, encodeId=20ff2110802d0, content=审稿速度:2.0 | 投稿命中率:50.0 经验分享：分了三个审稿人，一共给了33条意见，提的问题还是非常专业的。, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=64, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=7d3a8452177, createdName=ms4000000717792052, createdTime=Thu Jan 19 01:13:36 CST 2023, time=2023-01-19, status=1, ipAttribution=河南省), GetPortalCommentsPageByObjectIdResponse(id=1101566, encodeId=4f4711015661d, content=是否一定要按照结果的顺序撰写方法的顺序？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=83, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220301/6252e946e5ef413b86dc0f7d1f73b6db/9a7e8c88a38e487b8da4968a67bbd261.png, createdBy=b9f692910, createdName=rayms, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1101565, encodeId=483c1101565b2, content=方法的撰写顺序/逻辑顺序是什么？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=76, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=1ff65466529, createdName=ms6000000264964247, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860705, encodeId=a1ef860e05bd, content=审稿速度:4.0 经验分享：该刊物质量水平还是蛮高的，比很多ieee trans要好。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=137, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=72925414015, createdName=ms8775166821456669, createdTime=Tue May 19 00:38:24 CST 2020, time=2020-05-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860696, encodeId=934186069667, content=审稿速度:2.0 经验分享：你真不容易，要我就换期刊了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=27a45414023, createdName=ms2983094698733738, createdTime=Mon May 18 19:40:43 CST 2020, time=2020-05-18, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=859114, encodeId=e975859114a8, content=审稿非常严格，虽然收费，但感觉并不容易发.三个审稿人提了好多修改意见，大修两次、小修三次～～都改吐血了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=148, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=3de05414115, createdName=ms313574960747337, createdTime=Sun Apr 05 12:38:15 CST 2020, time=2020-04-05, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=858909, encodeId=367585890922, content=审稿速度:1.0 经验分享：received date 21 february 2020 revised date 25 march 2020 accepted date 28 march 2020 熵的跨学科应用，一审大修，专家意见一周左右返回，主编decision花了十天左右，然后大修给十天。大修提交后一周内返回小修后录用。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=b6e05414152, createdName=ms2614478266438915, createdTime=Mon Mar 30 16:57:11 CST 2020, time=2020-03-30, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855744, encodeId=4899855e441c, content=递交稿件，他又说试验样本太少，坚决拒稿。我也是醉了。我只是使用熵值方法来区分我的刺激诱发的神经活动，不是去研究机器学习算法的，现在变成了仅仅很少一部分是关于熵的，满篇都是特征提取、特征选择和机器学习等算法对比研究。让我加入八个算法进去，最后又说我试验样本太少拒稿。20个受试，每个受试做20组，每组10个试次，这个样本不算少了吧，即使使用机器学习分类也够了吧。结果花了两个月大改了2次，他又来了一句话样本太少强烈拒稿。我也是醉了，欲哭无泪！期刊再好，运气不好遇到个极品审稿人，不仅劳民伤财，最后也是遭罪。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=129, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=1e295414102, createdName=ms4512939187244456, createdTime=Thu Dec 12 15:30:37 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855743, encodeId=faaf855e437f, content=期刊编辑很好，审稿速度也挺快的，就是有的审稿专家比较较真。投了一篇关于新型熵方法区分和识别某种神经信号。三个审稿人，两个审稿人给的意见非常正面而且同意接收，最后一个审稿人非常较真和固执，提了一大堆意见，硬是让我加入他的特征选择算法，并要求增加其他三个特征选择算法进行对比。我按照他的要求认认真真地修改，文章基本上重新写了一遍，硬生生从开始的14页增加到20页。提交之后，他又说svm不够精确，要我再加入随机森林、lda算法，和“留一法”与之前的“十折交叉验证”对比。我又按照要求文章增加到26页。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=120, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=d2bd5414155, createdName=ms4729674192825029, createdTime=Thu Dec 12 15:30:06 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=852889, encodeId=6f96852889d9, content=received date 14 may 2019 revised date 6 july 2019 accepted date 10 july 2019 published date 13 july 2019 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=132, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=87eb5414016, createdName=ms4817431182430527, createdTime=Tue Jul 30 13:23:16 CST 2019, time=2019-07-30, status=1, ipAttribution=)]

2023-01-19 ms4000000717792052 来自河南省

审稿速度:2.0 | 投稿命中率:50.0
经验分享：分了三个审稿人，一共给了33条意见，提的问题还是非常专业的。

64 0

[GetPortalCommentsPageByObjectIdResponse(id=2110802, encodeId=20ff2110802d0, content=审稿速度:2.0 | 投稿命中率:50.0 经验分享：分了三个审稿人，一共给了33条意见，提的问题还是非常专业的。, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=64, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=7d3a8452177, createdName=ms4000000717792052, createdTime=Thu Jan 19 01:13:36 CST 2023, time=2023-01-19, status=1, ipAttribution=河南省), GetPortalCommentsPageByObjectIdResponse(id=1101566, encodeId=4f4711015661d, content=是否一定要按照结果的顺序撰写方法的顺序？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=83, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220301/6252e946e5ef413b86dc0f7d1f73b6db/9a7e8c88a38e487b8da4968a67bbd261.png, createdBy=b9f692910, createdName=rayms, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1101565, encodeId=483c1101565b2, content=方法的撰写顺序/逻辑顺序是什么？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=76, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=1ff65466529, createdName=ms6000000264964247, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860705, encodeId=a1ef860e05bd, content=审稿速度:4.0 经验分享：该刊物质量水平还是蛮高的，比很多ieee trans要好。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=137, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=72925414015, createdName=ms8775166821456669, createdTime=Tue May 19 00:38:24 CST 2020, time=2020-05-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860696, encodeId=934186069667, content=审稿速度:2.0 经验分享：你真不容易，要我就换期刊了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=27a45414023, createdName=ms2983094698733738, createdTime=Mon May 18 19:40:43 CST 2020, time=2020-05-18, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=859114, encodeId=e975859114a8, content=审稿非常严格，虽然收费，但感觉并不容易发.三个审稿人提了好多修改意见，大修两次、小修三次～～都改吐血了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=148, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=3de05414115, createdName=ms313574960747337, createdTime=Sun Apr 05 12:38:15 CST 2020, time=2020-04-05, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=858909, encodeId=367585890922, content=审稿速度:1.0 经验分享：received date 21 february 2020 revised date 25 march 2020 accepted date 28 march 2020 熵的跨学科应用，一审大修，专家意见一周左右返回，主编decision花了十天左右，然后大修给十天。大修提交后一周内返回小修后录用。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=b6e05414152, createdName=ms2614478266438915, createdTime=Mon Mar 30 16:57:11 CST 2020, time=2020-03-30, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855744, encodeId=4899855e441c, content=递交稿件，他又说试验样本太少，坚决拒稿。我也是醉了。我只是使用熵值方法来区分我的刺激诱发的神经活动，不是去研究机器学习算法的，现在变成了仅仅很少一部分是关于熵的，满篇都是特征提取、特征选择和机器学习等算法对比研究。让我加入八个算法进去，最后又说我试验样本太少拒稿。20个受试，每个受试做20组，每组10个试次，这个样本不算少了吧，即使使用机器学习分类也够了吧。结果花了两个月大改了2次，他又来了一句话样本太少强烈拒稿。我也是醉了，欲哭无泪！期刊再好，运气不好遇到个极品审稿人，不仅劳民伤财，最后也是遭罪。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=129, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=1e295414102, createdName=ms4512939187244456, createdTime=Thu Dec 12 15:30:37 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855743, encodeId=faaf855e437f, content=期刊编辑很好，审稿速度也挺快的，就是有的审稿专家比较较真。投了一篇关于新型熵方法区分和识别某种神经信号。三个审稿人，两个审稿人给的意见非常正面而且同意接收，最后一个审稿人非常较真和固执，提了一大堆意见，硬是让我加入他的特征选择算法，并要求增加其他三个特征选择算法进行对比。我按照他的要求认认真真地修改，文章基本上重新写了一遍，硬生生从开始的14页增加到20页。提交之后，他又说svm不够精确，要我再加入随机森林、lda算法，和“留一法”与之前的“十折交叉验证”对比。我又按照要求文章增加到26页。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=120, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=d2bd5414155, createdName=ms4729674192825029, createdTime=Thu Dec 12 15:30:06 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=852889, encodeId=6f96852889d9, content=received date 14 may 2019 revised date 6 july 2019 accepted date 10 july 2019 published date 13 july 2019 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=132, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=87eb5414016, createdName=ms4817431182430527, createdTime=Tue Jul 30 13:23:16 CST 2019, time=2019-07-30, status=1, ipAttribution=)]

2021-12-29 rayms

是否一定要按照结果的顺序撰写方法的顺序？

83 1

展开1条回复

[GetPortalCommentsPageByObjectIdResponse(id=2110802, encodeId=20ff2110802d0, content=审稿速度:2.0 | 投稿命中率:50.0 经验分享：分了三个审稿人，一共给了33条意见，提的问题还是非常专业的。, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=64, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=7d3a8452177, createdName=ms4000000717792052, createdTime=Thu Jan 19 01:13:36 CST 2023, time=2023-01-19, status=1, ipAttribution=河南省), GetPortalCommentsPageByObjectIdResponse(id=1101566, encodeId=4f4711015661d, content=是否一定要按照结果的顺序撰写方法的顺序？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=83, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220301/6252e946e5ef413b86dc0f7d1f73b6db/9a7e8c88a38e487b8da4968a67bbd261.png, createdBy=b9f692910, createdName=rayms, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1101565, encodeId=483c1101565b2, content=方法的撰写顺序/逻辑顺序是什么？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=76, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=1ff65466529, createdName=ms6000000264964247, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860705, encodeId=a1ef860e05bd, content=审稿速度:4.0 经验分享：该刊物质量水平还是蛮高的，比很多ieee trans要好。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=137, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=72925414015, createdName=ms8775166821456669, createdTime=Tue May 19 00:38:24 CST 2020, time=2020-05-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860696, encodeId=934186069667, content=审稿速度:2.0 经验分享：你真不容易，要我就换期刊了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=27a45414023, createdName=ms2983094698733738, createdTime=Mon May 18 19:40:43 CST 2020, time=2020-05-18, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=859114, encodeId=e975859114a8, content=审稿非常严格，虽然收费，但感觉并不容易发.三个审稿人提了好多修改意见，大修两次、小修三次～～都改吐血了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=148, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=3de05414115, createdName=ms313574960747337, createdTime=Sun Apr 05 12:38:15 CST 2020, time=2020-04-05, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=858909, encodeId=367585890922, content=审稿速度:1.0 经验分享：received date 21 february 2020 revised date 25 march 2020 accepted date 28 march 2020 熵的跨学科应用，一审大修，专家意见一周左右返回，主编decision花了十天左右，然后大修给十天。大修提交后一周内返回小修后录用。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=b6e05414152, createdName=ms2614478266438915, createdTime=Mon Mar 30 16:57:11 CST 2020, time=2020-03-30, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855744, encodeId=4899855e441c, content=递交稿件，他又说试验样本太少，坚决拒稿。我也是醉了。我只是使用熵值方法来区分我的刺激诱发的神经活动，不是去研究机器学习算法的，现在变成了仅仅很少一部分是关于熵的，满篇都是特征提取、特征选择和机器学习等算法对比研究。让我加入八个算法进去，最后又说我试验样本太少拒稿。20个受试，每个受试做20组，每组10个试次，这个样本不算少了吧，即使使用机器学习分类也够了吧。结果花了两个月大改了2次，他又来了一句话样本太少强烈拒稿。我也是醉了，欲哭无泪！期刊再好，运气不好遇到个极品审稿人，不仅劳民伤财，最后也是遭罪。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=129, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=1e295414102, createdName=ms4512939187244456, createdTime=Thu Dec 12 15:30:37 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855743, encodeId=faaf855e437f, content=期刊编辑很好，审稿速度也挺快的，就是有的审稿专家比较较真。投了一篇关于新型熵方法区分和识别某种神经信号。三个审稿人，两个审稿人给的意见非常正面而且同意接收，最后一个审稿人非常较真和固执，提了一大堆意见，硬是让我加入他的特征选择算法，并要求增加其他三个特征选择算法进行对比。我按照他的要求认认真真地修改，文章基本上重新写了一遍，硬生生从开始的14页增加到20页。提交之后，他又说svm不够精确，要我再加入随机森林、lda算法，和“留一法”与之前的“十折交叉验证”对比。我又按照要求文章增加到26页。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=120, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=d2bd5414155, createdName=ms4729674192825029, createdTime=Thu Dec 12 15:30:06 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=852889, encodeId=6f96852889d9, content=received date 14 may 2019 revised date 6 july 2019 accepted date 10 july 2019 published date 13 july 2019 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=132, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=87eb5414016, createdName=ms4817431182430527, createdTime=Tue Jul 30 13:23:16 CST 2019, time=2019-07-30, status=1, ipAttribution=)]

2021-12-29 ms6000000264964247

方法的撰写顺序/逻辑顺序是什么？

76 1

展开1条回复

[GetPortalCommentsPageByObjectIdResponse(id=2110802, encodeId=20ff2110802d0, content=审稿速度:2.0 | 投稿命中率:50.0 经验分享：分了三个审稿人，一共给了33条意见，提的问题还是非常专业的。, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=64, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=7d3a8452177, createdName=ms4000000717792052, createdTime=Thu Jan 19 01:13:36 CST 2023, time=2023-01-19, status=1, ipAttribution=河南省), GetPortalCommentsPageByObjectIdResponse(id=1101566, encodeId=4f4711015661d, content=是否一定要按照结果的顺序撰写方法的顺序？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=83, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220301/6252e946e5ef413b86dc0f7d1f73b6db/9a7e8c88a38e487b8da4968a67bbd261.png, createdBy=b9f692910, createdName=rayms, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1101565, encodeId=483c1101565b2, content=方法的撰写顺序/逻辑顺序是什么？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=76, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=1ff65466529, createdName=ms6000000264964247, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860705, encodeId=a1ef860e05bd, content=审稿速度:4.0 经验分享：该刊物质量水平还是蛮高的，比很多ieee trans要好。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=137, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=72925414015, createdName=ms8775166821456669, createdTime=Tue May 19 00:38:24 CST 2020, time=2020-05-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860696, encodeId=934186069667, content=审稿速度:2.0 经验分享：你真不容易，要我就换期刊了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=27a45414023, createdName=ms2983094698733738, createdTime=Mon May 18 19:40:43 CST 2020, time=2020-05-18, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=859114, encodeId=e975859114a8, content=审稿非常严格，虽然收费，但感觉并不容易发.三个审稿人提了好多修改意见，大修两次、小修三次～～都改吐血了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=148, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=3de05414115, createdName=ms313574960747337, createdTime=Sun Apr 05 12:38:15 CST 2020, time=2020-04-05, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=858909, encodeId=367585890922, content=审稿速度:1.0 经验分享：received date 21 february 2020 revised date 25 march 2020 accepted date 28 march 2020 熵的跨学科应用，一审大修，专家意见一周左右返回，主编decision花了十天左右，然后大修给十天。大修提交后一周内返回小修后录用。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=b6e05414152, createdName=ms2614478266438915, createdTime=Mon Mar 30 16:57:11 CST 2020, time=2020-03-30, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855744, encodeId=4899855e441c, content=递交稿件，他又说试验样本太少，坚决拒稿。我也是醉了。我只是使用熵值方法来区分我的刺激诱发的神经活动，不是去研究机器学习算法的，现在变成了仅仅很少一部分是关于熵的，满篇都是特征提取、特征选择和机器学习等算法对比研究。让我加入八个算法进去，最后又说我试验样本太少拒稿。20个受试，每个受试做20组，每组10个试次，这个样本不算少了吧，即使使用机器学习分类也够了吧。结果花了两个月大改了2次，他又来了一句话样本太少强烈拒稿。我也是醉了，欲哭无泪！期刊再好，运气不好遇到个极品审稿人，不仅劳民伤财，最后也是遭罪。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=129, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=1e295414102, createdName=ms4512939187244456, createdTime=Thu Dec 12 15:30:37 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855743, encodeId=faaf855e437f, content=期刊编辑很好，审稿速度也挺快的，就是有的审稿专家比较较真。投了一篇关于新型熵方法区分和识别某种神经信号。三个审稿人，两个审稿人给的意见非常正面而且同意接收，最后一个审稿人非常较真和固执，提了一大堆意见，硬是让我加入他的特征选择算法，并要求增加其他三个特征选择算法进行对比。我按照他的要求认认真真地修改，文章基本上重新写了一遍，硬生生从开始的14页增加到20页。提交之后，他又说svm不够精确，要我再加入随机森林、lda算法，和“留一法”与之前的“十折交叉验证”对比。我又按照要求文章增加到26页。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=120, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=d2bd5414155, createdName=ms4729674192825029, createdTime=Thu Dec 12 15:30:06 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=852889, encodeId=6f96852889d9, content=received date 14 may 2019 revised date 6 july 2019 accepted date 10 july 2019 published date 13 july 2019 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=132, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=87eb5414016, createdName=ms4817431182430527, createdTime=Tue Jul 30 13:23:16 CST 2019, time=2019-07-30, status=1, ipAttribution=)]

2020-05-19 ms8775166821456669

审稿速度:4.0
经验分享：该刊物质量水平还是蛮高的，比很多ieee trans要好。

137 0

[GetPortalCommentsPageByObjectIdResponse(id=2110802, encodeId=20ff2110802d0, content=审稿速度:2.0 | 投稿命中率:50.0 经验分享：分了三个审稿人，一共给了33条意见，提的问题还是非常专业的。, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=64, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=7d3a8452177, createdName=ms4000000717792052, createdTime=Thu Jan 19 01:13:36 CST 2023, time=2023-01-19, status=1, ipAttribution=河南省), GetPortalCommentsPageByObjectIdResponse(id=1101566, encodeId=4f4711015661d, content=是否一定要按照结果的顺序撰写方法的顺序？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=83, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220301/6252e946e5ef413b86dc0f7d1f73b6db/9a7e8c88a38e487b8da4968a67bbd261.png, createdBy=b9f692910, createdName=rayms, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1101565, encodeId=483c1101565b2, content=方法的撰写顺序/逻辑顺序是什么？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=76, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=1ff65466529, createdName=ms6000000264964247, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860705, encodeId=a1ef860e05bd, content=审稿速度:4.0 经验分享：该刊物质量水平还是蛮高的，比很多ieee trans要好。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=137, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=72925414015, createdName=ms8775166821456669, createdTime=Tue May 19 00:38:24 CST 2020, time=2020-05-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860696, encodeId=934186069667, content=审稿速度:2.0 经验分享：你真不容易，要我就换期刊了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=27a45414023, createdName=ms2983094698733738, createdTime=Mon May 18 19:40:43 CST 2020, time=2020-05-18, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=859114, encodeId=e975859114a8, content=审稿非常严格，虽然收费，但感觉并不容易发.三个审稿人提了好多修改意见，大修两次、小修三次～～都改吐血了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=148, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=3de05414115, createdName=ms313574960747337, createdTime=Sun Apr 05 12:38:15 CST 2020, time=2020-04-05, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=858909, encodeId=367585890922, content=审稿速度:1.0 经验分享：received date 21 february 2020 revised date 25 march 2020 accepted date 28 march 2020 熵的跨学科应用，一审大修，专家意见一周左右返回，主编decision花了十天左右，然后大修给十天。大修提交后一周内返回小修后录用。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=b6e05414152, createdName=ms2614478266438915, createdTime=Mon Mar 30 16:57:11 CST 2020, time=2020-03-30, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855744, encodeId=4899855e441c, content=递交稿件，他又说试验样本太少，坚决拒稿。我也是醉了。我只是使用熵值方法来区分我的刺激诱发的神经活动，不是去研究机器学习算法的，现在变成了仅仅很少一部分是关于熵的，满篇都是特征提取、特征选择和机器学习等算法对比研究。让我加入八个算法进去，最后又说我试验样本太少拒稿。20个受试，每个受试做20组，每组10个试次，这个样本不算少了吧，即使使用机器学习分类也够了吧。结果花了两个月大改了2次，他又来了一句话样本太少强烈拒稿。我也是醉了，欲哭无泪！期刊再好，运气不好遇到个极品审稿人，不仅劳民伤财，最后也是遭罪。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=129, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=1e295414102, createdName=ms4512939187244456, createdTime=Thu Dec 12 15:30:37 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855743, encodeId=faaf855e437f, content=期刊编辑很好，审稿速度也挺快的，就是有的审稿专家比较较真。投了一篇关于新型熵方法区分和识别某种神经信号。三个审稿人，两个审稿人给的意见非常正面而且同意接收，最后一个审稿人非常较真和固执，提了一大堆意见，硬是让我加入他的特征选择算法，并要求增加其他三个特征选择算法进行对比。我按照他的要求认认真真地修改，文章基本上重新写了一遍，硬生生从开始的14页增加到20页。提交之后，他又说svm不够精确，要我再加入随机森林、lda算法，和“留一法”与之前的“十折交叉验证”对比。我又按照要求文章增加到26页。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=120, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=d2bd5414155, createdName=ms4729674192825029, createdTime=Thu Dec 12 15:30:06 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=852889, encodeId=6f96852889d9, content=received date 14 may 2019 revised date 6 july 2019 accepted date 10 july 2019 published date 13 july 2019 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=132, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=87eb5414016, createdName=ms4817431182430527, createdTime=Tue Jul 30 13:23:16 CST 2019, time=2019-07-30, status=1, ipAttribution=)]

2020-05-18 ms2983094698733738

审稿速度:2.0
经验分享：你真不容易，要我就换期刊了

0 0

[GetPortalCommentsPageByObjectIdResponse(id=2110802, encodeId=20ff2110802d0, content=审稿速度:2.0 | 投稿命中率:50.0 经验分享：分了三个审稿人，一共给了33条意见，提的问题还是非常专业的。, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=64, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=7d3a8452177, createdName=ms4000000717792052, createdTime=Thu Jan 19 01:13:36 CST 2023, time=2023-01-19, status=1, ipAttribution=河南省), GetPortalCommentsPageByObjectIdResponse(id=1101566, encodeId=4f4711015661d, content=是否一定要按照结果的顺序撰写方法的顺序？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=83, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220301/6252e946e5ef413b86dc0f7d1f73b6db/9a7e8c88a38e487b8da4968a67bbd261.png, createdBy=b9f692910, createdName=rayms, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1101565, encodeId=483c1101565b2, content=方法的撰写顺序/逻辑顺序是什么？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=76, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=1ff65466529, createdName=ms6000000264964247, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860705, encodeId=a1ef860e05bd, content=审稿速度:4.0 经验分享：该刊物质量水平还是蛮高的，比很多ieee trans要好。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=137, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=72925414015, createdName=ms8775166821456669, createdTime=Tue May 19 00:38:24 CST 2020, time=2020-05-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860696, encodeId=934186069667, content=审稿速度:2.0 经验分享：你真不容易，要我就换期刊了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=27a45414023, createdName=ms2983094698733738, createdTime=Mon May 18 19:40:43 CST 2020, time=2020-05-18, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=859114, encodeId=e975859114a8, content=审稿非常严格，虽然收费，但感觉并不容易发.三个审稿人提了好多修改意见，大修两次、小修三次～～都改吐血了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=148, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=3de05414115, createdName=ms313574960747337, createdTime=Sun Apr 05 12:38:15 CST 2020, time=2020-04-05, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=858909, encodeId=367585890922, content=审稿速度:1.0 经验分享：received date 21 february 2020 revised date 25 march 2020 accepted date 28 march 2020 熵的跨学科应用，一审大修，专家意见一周左右返回，主编decision花了十天左右，然后大修给十天。大修提交后一周内返回小修后录用。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=b6e05414152, createdName=ms2614478266438915, createdTime=Mon Mar 30 16:57:11 CST 2020, time=2020-03-30, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855744, encodeId=4899855e441c, content=递交稿件，他又说试验样本太少，坚决拒稿。我也是醉了。我只是使用熵值方法来区分我的刺激诱发的神经活动，不是去研究机器学习算法的，现在变成了仅仅很少一部分是关于熵的，满篇都是特征提取、特征选择和机器学习等算法对比研究。让我加入八个算法进去，最后又说我试验样本太少拒稿。20个受试，每个受试做20组，每组10个试次，这个样本不算少了吧，即使使用机器学习分类也够了吧。结果花了两个月大改了2次，他又来了一句话样本太少强烈拒稿。我也是醉了，欲哭无泪！期刊再好，运气不好遇到个极品审稿人，不仅劳民伤财，最后也是遭罪。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=129, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=1e295414102, createdName=ms4512939187244456, createdTime=Thu Dec 12 15:30:37 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855743, encodeId=faaf855e437f, content=期刊编辑很好，审稿速度也挺快的，就是有的审稿专家比较较真。投了一篇关于新型熵方法区分和识别某种神经信号。三个审稿人，两个审稿人给的意见非常正面而且同意接收，最后一个审稿人非常较真和固执，提了一大堆意见，硬是让我加入他的特征选择算法，并要求增加其他三个特征选择算法进行对比。我按照他的要求认认真真地修改，文章基本上重新写了一遍，硬生生从开始的14页增加到20页。提交之后，他又说svm不够精确，要我再加入随机森林、lda算法，和“留一法”与之前的“十折交叉验证”对比。我又按照要求文章增加到26页。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=120, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=d2bd5414155, createdName=ms4729674192825029, createdTime=Thu Dec 12 15:30:06 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=852889, encodeId=6f96852889d9, content=received date 14 may 2019 revised date 6 july 2019 accepted date 10 july 2019 published date 13 july 2019 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=132, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=87eb5414016, createdName=ms4817431182430527, createdTime=Tue Jul 30 13:23:16 CST 2019, time=2019-07-30, status=1, ipAttribution=)]

2020-04-05 ms313574960747337

审稿非常严格，虽然收费，但感觉并不容易发.三个审稿人提了好多修改意见，大修两次、小修三次～～都改吐血了

148 0

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time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1101565, encodeId=483c1101565b2, content=方法的撰写顺序/逻辑顺序是什么？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=76, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=1ff65466529, createdName=ms6000000264964247, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860705, encodeId=a1ef860e05bd, content=审稿速度:4.0 经验分享：该刊物质量水平还是蛮高的，比很多ieee trans要好。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=137, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=72925414015, createdName=ms8775166821456669, createdTime=Tue May 19 00:38:24 CST 2020, time=2020-05-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860696, encodeId=934186069667, content=审稿速度:2.0 经验分享：你真不容易，要我就换期刊了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=27a45414023, createdName=ms2983094698733738, createdTime=Mon May 18 19:40:43 CST 2020, time=2020-05-18, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=859114, encodeId=e975859114a8, content=审稿非常严格，虽然收费，但感觉并不容易发.三个审稿人提了好多修改意见，大修两次、小修三次～～都改吐血了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=148, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=3de05414115, createdName=ms313574960747337, createdTime=Sun Apr 05 12:38:15 CST 2020, time=2020-04-05, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=858909, encodeId=367585890922, content=审稿速度:1.0 经验分享：received date 21 february 2020 revised date 25 march 2020 accepted date 28 march 2020 熵的跨学科应用，一审大修，专家意见一周左右返回，主编decision花了十天左右，然后大修给十天。大修提交后一周内返回小修后录用。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=b6e05414152, createdName=ms2614478266438915, createdTime=Mon Mar 30 16:57:11 CST 2020, time=2020-03-30, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855744, encodeId=4899855e441c, content=递交稿件，他又说试验样本太少，坚决拒稿。我也是醉了。我只是使用熵值方法来区分我的刺激诱发的神经活动，不是去研究机器学习算法的，现在变成了仅仅很少一部分是关于熵的，满篇都是特征提取、特征选择和机器学习等算法对比研究。让我加入八个算法进去，最后又说我试验样本太少拒稿。20个受试，每个受试做20组，每组10个试次，这个样本不算少了吧，即使使用机器学习分类也够了吧。结果花了两个月大改了2次，他又来了一句话样本太少强烈拒稿。我也是醉了，欲哭无泪！期刊再好，运气不好遇到个极品审稿人，不仅劳民伤财，最后也是遭罪。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=129, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=1e295414102, createdName=ms4512939187244456, createdTime=Thu Dec 12 15:30:37 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855743, encodeId=faaf855e437f, content=期刊编辑很好，审稿速度也挺快的，就是有的审稿专家比较较真。投了一篇关于新型熵方法区分和识别某种神经信号。三个审稿人，两个审稿人给的意见非常正面而且同意接收，最后一个审稿人非常较真和固执，提了一大堆意见，硬是让我加入他的特征选择算法，并要求增加其他三个特征选择算法进行对比。我按照他的要求认认真真地修改，文章基本上重新写了一遍，硬生生从开始的14页增加到20页。提交之后，他又说svm不够精确，要我再加入随机森林、lda算法，和“留一法”与之前的“十折交叉验证”对比。我又按照要求文章增加到26页。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=120, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=d2bd5414155, createdName=ms4729674192825029, createdTime=Thu Dec 12 15:30:06 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=852889, encodeId=6f96852889d9, content=received date 14 may 2019 revised date 6 july 2019 accepted date 10 july 2019 published date 13 july 2019 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=132, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=87eb5414016, createdName=ms4817431182430527, createdTime=Tue Jul 30 13:23:16 CST 2019, time=2019-07-30, status=1, ipAttribution=)]

2020-03-30 ms2614478266438915

审稿速度:1.0
经验分享：received date
21 february 2020
revised date
25 march 2020
accepted date
28 march 2020
熵的跨学科应用，一审大修，专家意见一周左右返回，主编decision花了十天左右，然后大修给十天。大修提交后一周内返回小修后录用。

0 0

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time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1101565, encodeId=483c1101565b2, content=方法的撰写顺序/逻辑顺序是什么？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=76, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=1ff65466529, createdName=ms6000000264964247, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860705, encodeId=a1ef860e05bd, content=审稿速度:4.0 经验分享：该刊物质量水平还是蛮高的，比很多ieee trans要好。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=137, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=72925414015, createdName=ms8775166821456669, createdTime=Tue May 19 00:38:24 CST 2020, time=2020-05-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860696, encodeId=934186069667, content=审稿速度:2.0 经验分享：你真不容易，要我就换期刊了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=27a45414023, createdName=ms2983094698733738, createdTime=Mon May 18 19:40:43 CST 2020, time=2020-05-18, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=859114, encodeId=e975859114a8, content=审稿非常严格，虽然收费，但感觉并不容易发.三个审稿人提了好多修改意见，大修两次、小修三次～～都改吐血了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=148, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=3de05414115, createdName=ms313574960747337, createdTime=Sun Apr 05 12:38:15 CST 2020, time=2020-04-05, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=858909, encodeId=367585890922, content=审稿速度:1.0 经验分享：received date 21 february 2020 revised date 25 march 2020 accepted date 28 march 2020 熵的跨学科应用，一审大修，专家意见一周左右返回，主编decision花了十天左右，然后大修给十天。大修提交后一周内返回小修后录用。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=b6e05414152, createdName=ms2614478266438915, createdTime=Mon Mar 30 16:57:11 CST 2020, time=2020-03-30, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855744, encodeId=4899855e441c, content=递交稿件，他又说试验样本太少，坚决拒稿。我也是醉了。我只是使用熵值方法来区分我的刺激诱发的神经活动，不是去研究机器学习算法的，现在变成了仅仅很少一部分是关于熵的，满篇都是特征提取、特征选择和机器学习等算法对比研究。让我加入八个算法进去，最后又说我试验样本太少拒稿。20个受试，每个受试做20组，每组10个试次，这个样本不算少了吧，即使使用机器学习分类也够了吧。结果花了两个月大改了2次，他又来了一句话样本太少强烈拒稿。我也是醉了，欲哭无泪！期刊再好，运气不好遇到个极品审稿人，不仅劳民伤财，最后也是遭罪。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=129, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=1e295414102, createdName=ms4512939187244456, createdTime=Thu Dec 12 15:30:37 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855743, encodeId=faaf855e437f, content=期刊编辑很好，审稿速度也挺快的，就是有的审稿专家比较较真。投了一篇关于新型熵方法区分和识别某种神经信号。三个审稿人，两个审稿人给的意见非常正面而且同意接收，最后一个审稿人非常较真和固执，提了一大堆意见，硬是让我加入他的特征选择算法，并要求增加其他三个特征选择算法进行对比。我按照他的要求认认真真地修改，文章基本上重新写了一遍，硬生生从开始的14页增加到20页。提交之后，他又说svm不够精确，要我再加入随机森林、lda算法，和“留一法”与之前的“十折交叉验证”对比。我又按照要求文章增加到26页。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=120, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=d2bd5414155, createdName=ms4729674192825029, createdTime=Thu Dec 12 15:30:06 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=852889, encodeId=6f96852889d9, content=received date 14 may 2019 revised date 6 july 2019 accepted date 10 july 2019 published date 13 july 2019 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=132, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=87eb5414016, createdName=ms4817431182430527, createdTime=Tue Jul 30 13:23:16 CST 2019, time=2019-07-30, status=1, ipAttribution=)]

2019-12-12 ms4512939187244456

递交稿件，他又说试验样本太少，坚决拒稿。我也是醉了。我只是使用熵值方法来区分我的刺激诱发的神经活动，不是去研究机器学习算法的，现在变成了仅仅很少一部分是关于熵的，满篇都是特征提取、特征选择和机器学习等算法对比研究。让我加入八个算法进去，最后又说我试验样本太少拒稿。20个受试，每个受试做20组，每组10个试次，这个样本不算少了吧，即使使用机器学习分类也够了吧。结果花了两个月大改了2次，他又来了一句话样本太少强烈拒稿。我也是醉了，欲哭无泪！期刊再好，运气不好遇到个极品审稿人，不仅劳民伤财，最后也是遭罪。

129 0

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time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1101565, encodeId=483c1101565b2, content=方法的撰写顺序/逻辑顺序是什么？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=76, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=1ff65466529, createdName=ms6000000264964247, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860705, encodeId=a1ef860e05bd, content=审稿速度:4.0 经验分享：该刊物质量水平还是蛮高的，比很多ieee trans要好。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=137, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=72925414015, createdName=ms8775166821456669, createdTime=Tue May 19 00:38:24 CST 2020, time=2020-05-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860696, encodeId=934186069667, content=审稿速度:2.0 经验分享：你真不容易，要我就换期刊了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=27a45414023, createdName=ms2983094698733738, createdTime=Mon May 18 19:40:43 CST 2020, time=2020-05-18, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=859114, encodeId=e975859114a8, content=审稿非常严格，虽然收费，但感觉并不容易发.三个审稿人提了好多修改意见，大修两次、小修三次～～都改吐血了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=148, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=3de05414115, createdName=ms313574960747337, createdTime=Sun Apr 05 12:38:15 CST 2020, time=2020-04-05, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=858909, encodeId=367585890922, content=审稿速度:1.0 经验分享：received date 21 february 2020 revised date 25 march 2020 accepted date 28 march 2020 熵的跨学科应用，一审大修，专家意见一周左右返回，主编decision花了十天左右，然后大修给十天。大修提交后一周内返回小修后录用。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=b6e05414152, createdName=ms2614478266438915, createdTime=Mon Mar 30 16:57:11 CST 2020, time=2020-03-30, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855744, encodeId=4899855e441c, content=递交稿件，他又说试验样本太少，坚决拒稿。我也是醉了。我只是使用熵值方法来区分我的刺激诱发的神经活动，不是去研究机器学习算法的，现在变成了仅仅很少一部分是关于熵的，满篇都是特征提取、特征选择和机器学习等算法对比研究。让我加入八个算法进去，最后又说我试验样本太少拒稿。20个受试，每个受试做20组，每组10个试次，这个样本不算少了吧，即使使用机器学习分类也够了吧。结果花了两个月大改了2次，他又来了一句话样本太少强烈拒稿。我也是醉了，欲哭无泪！期刊再好，运气不好遇到个极品审稿人，不仅劳民伤财，最后也是遭罪。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=129, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=1e295414102, createdName=ms4512939187244456, createdTime=Thu Dec 12 15:30:37 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855743, encodeId=faaf855e437f, content=期刊编辑很好，审稿速度也挺快的，就是有的审稿专家比较较真。投了一篇关于新型熵方法区分和识别某种神经信号。三个审稿人，两个审稿人给的意见非常正面而且同意接收，最后一个审稿人非常较真和固执，提了一大堆意见，硬是让我加入他的特征选择算法，并要求增加其他三个特征选择算法进行对比。我按照他的要求认认真真地修改，文章基本上重新写了一遍，硬生生从开始的14页增加到20页。提交之后，他又说svm不够精确，要我再加入随机森林、lda算法，和“留一法”与之前的“十折交叉验证”对比。我又按照要求文章增加到26页。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=120, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=d2bd5414155, createdName=ms4729674192825029, createdTime=Thu Dec 12 15:30:06 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=852889, encodeId=6f96852889d9, content=received date 14 may 2019 revised date 6 july 2019 accepted date 10 july 2019 published date 13 july 2019 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=132, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=87eb5414016, createdName=ms4817431182430527, createdTime=Tue Jul 30 13:23:16 CST 2019, time=2019-07-30, status=1, ipAttribution=)]

2019-12-12 ms4729674192825029

期刊编辑很好，审稿速度也挺快的，就是有的审稿专家比较较真。投了一篇关于新型熵方法区分和识别某种神经信号。三个审稿人，两个审稿人给的意见非常正面而且同意接收，最后一个审稿人非常较真和固执，提了一大堆意见，硬是让我加入他的特征选择算法，并要求增加其他三个特征选择算法进行对比。我按照他的要求认认真真地修改，文章基本上重新写了一遍，硬生生从开始的14页增加到20页。提交之后，他又说svm不够精确，要我再加入随机森林、lda算法，和“留一法”与之前的“十折交叉验证”对比。我又按照要求文章增加到26页。

120 0

[GetPortalCommentsPageByObjectIdResponse(id=2110802, encodeId=20ff2110802d0, content=审稿速度:2.0 | 投稿命中率:50.0 经验分享：分了三个审稿人，一共给了33条意见，提的问题还是非常专业的。, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=64, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=7d3a8452177, createdName=ms4000000717792052, createdTime=Thu Jan 19 01:13:36 CST 2023, time=2023-01-19, status=1, ipAttribution=河南省), GetPortalCommentsPageByObjectIdResponse(id=1101566, encodeId=4f4711015661d, content=是否一定要按照结果的顺序撰写方法的顺序？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=83, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220301/6252e946e5ef413b86dc0f7d1f73b6db/9a7e8c88a38e487b8da4968a67bbd261.png, createdBy=b9f692910, createdName=rayms, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1101565, encodeId=483c1101565b2, content=方法的撰写顺序/逻辑顺序是什么？, beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=76, replyNumber=1, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=/v1.0.0/img/user_icon.png, createdBy=1ff65466529, createdName=ms6000000264964247, createdTime=Wed Dec 29 14:52:51 CST 2021, time=2021-12-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860705, encodeId=a1ef860e05bd, content=审稿速度:4.0 经验分享：该刊物质量水平还是蛮高的，比很多ieee trans要好。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=137, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=72925414015, createdName=ms8775166821456669, createdTime=Tue May 19 00:38:24 CST 2020, time=2020-05-19, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=860696, encodeId=934186069667, content=审稿速度:2.0 经验分享：你真不容易，要我就换期刊了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=27a45414023, createdName=ms2983094698733738, createdTime=Mon May 18 19:40:43 CST 2020, time=2020-05-18, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=859114, encodeId=e975859114a8, content=审稿非常严格，虽然收费，但感觉并不容易发.三个审稿人提了好多修改意见，大修两次、小修三次～～都改吐血了 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=148, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=3de05414115, createdName=ms313574960747337, createdTime=Sun Apr 05 12:38:15 CST 2020, time=2020-04-05, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=858909, encodeId=367585890922, content=审稿速度:1.0 经验分享：received date 21 february 2020 revised date 25 march 2020 accepted date 28 march 2020 熵的跨学科应用，一审大修，专家意见一周左右返回，主编decision花了十天左右，然后大修给十天。大修提交后一周内返回小修后录用。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=0, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=b6e05414152, createdName=ms2614478266438915, createdTime=Mon Mar 30 16:57:11 CST 2020, time=2020-03-30, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855744, encodeId=4899855e441c, content=递交稿件，他又说试验样本太少，坚决拒稿。我也是醉了。我只是使用熵值方法来区分我的刺激诱发的神经活动，不是去研究机器学习算法的，现在变成了仅仅很少一部分是关于熵的，满篇都是特征提取、特征选择和机器学习等算法对比研究。让我加入八个算法进去，最后又说我试验样本太少拒稿。20个受试，每个受试做20组，每组10个试次，这个样本不算少了吧，即使使用机器学习分类也够了吧。结果花了两个月大改了2次，他又来了一句话样本太少强烈拒稿。我也是醉了，欲哭无泪！期刊再好，运气不好遇到个极品审稿人，不仅劳民伤财，最后也是遭罪。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=129, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=1e295414102, createdName=ms4512939187244456, createdTime=Thu Dec 12 15:30:37 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=855743, encodeId=faaf855e437f, content=期刊编辑很好，审稿速度也挺快的，就是有的审稿专家比较较真。投了一篇关于新型熵方法区分和识别某种神经信号。三个审稿人，两个审稿人给的意见非常正面而且同意接收，最后一个审稿人非常较真和固执，提了一大堆意见，硬是让我加入他的特征选择算法，并要求增加其他三个特征选择算法进行对比。我按照他的要求认认真真地修改，文章基本上重新写了一遍，硬生生从开始的14页增加到20页。提交之后，他又说svm不够精确，要我再加入随机森林、lda算法，和“留一法”与之前的“十折交叉验证”对比。我又按照要求文章增加到26页。 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=120, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=d2bd5414155, createdName=ms4729674192825029, createdTime=Thu Dec 12 15:30:06 CST 2019, time=2019-12-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=852889, encodeId=6f96852889d9, content=received date 14 may 2019 revised date 6 july 2019 accepted date 10 july 2019 published date 13 july 2019 , beContent=null, objectType=tool_impact_factor, channel=null, level=null, likeNumber=132, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=87eb5414016, createdName=ms4817431182430527, createdTime=Tue Jul 30 13:23:16 CST 2019, time=2019-07-30, status=1, ipAttribution=)]

2019-07-30 ms4817431182430527

received date 14 may 2019
revised date 6 july 2019
accepted date 10 july 2019
published date 13 july 2019

132 0

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